I went to a talk a few weeks ago by Richard Wilkinson and Kate Pickett, global health researchers who have written a book called The Spirit Level. They were quick to explain that, while the name makes perfect sense in British English, it has been a source of continuing confusion in American English. What is a “spirit level”? It’s a building tool, a type of ruler with little bubbles in it to show when it is parallel to the ground. Maybe it’s called a carpenter level in the states, or just a level when the context is clear.
I would have called it “Inequality vs Stuff”, or at least that’s my description of the talk: a vast array of scatterplots showing the relationship between income inequality and different measurements of population health. Here is one that is typical for their case:
When they told the story, they started with a composite health index scattered against inequality, since that has much less noise, and then use the noisy plots like this one as supporting evidence when they show that the relationship holds for everything.
The slide that stuck with me the most is one that diverged from their story a little:
Not population health this time, but still interesting. Something to share with your entrepreneur friends.
These plots seem like enough fun that I made my own, based on a question from the question and answer portion of the talk. I’ve forgotten who, but someone in the audience asked “How is inequality related to total fertility rate?” and the answer from Wilkinson and Pickett was along the lines of “We never thought to check, how do you think it might be related?”
Since I had the data lying around from my attempts to learn about model selection last summer, I made myself the plot. Turns out there is not much of an association.The only example of a non-association the speakers mentioned was a surprise to them: suicide rates are not correlated with income inequality.
I got some good news for the weekend, an opinion piece that I wrote together with some of the other post-graduate fellows at IHME was published online as a Science e-letter. It is titled U.S. Health Care Reform: The Case for Accountability and it’s about the measuring the outputs, outcomes, and impacts of the reform, whatever shape they end up taking.
The part that I was especially interested in adding to the discussion appears in paragraphs 3 and 4, about what these some of these statistics look like currently:
Disparities in health outcomes in the U.S. are unacceptable. A healthy life expectancy at birth in the U.S. ranks behind 28 other developed countries (1). Sizable groups in the United States have mortality risks resembling those in sub-Saharan Africa (2), including urban blacks between the ages of 15 and 64 living in counties with high homicide rates.
On average, Asian women lived 21 years longer than high-risk urban black males in 2001 (2). Although life expectancy for most American women increased between 1983 and 1999, life expectancy for women in 180 counties in areas such as Appalachia, the Deep South, the southern Midwest, and Texas decreased by 1.3 years (3).
I made some figures to accompany this, which Science didn’t print, so I’ve included them for you here:
Probability of a 45 year-old male dying before age 65, 2001, from Murray et al., Eight Americas: Investigating mortality disparities across races, counties, and race-counties in the United States. PLoS Medicine 2006.
Female life expectancy in US counties, 1961-1999 from Ezzati et al., The reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States. PLoS Medicine 2008.
It’s been snowing in Seattle for a week now, and that never happens. Things were already getting quiet around here for the holidays, but now there are almost no cars on the roads and it’s been really quiet. I’ve been watching healthy algorithm videos to pass the nice, quiet time: